Artificial Intelligence in Pharma and Care Delivery

Commentary

Julie Carty

Delivering on the Promise

In 2017, the promise of Artificial Intelligence (AI) to transform the healthcare industry is as bold as ever, despite growing confusion over the state of progress, adoption, and competition in this rapidly growing field. HealthXL recently commissioned a brief exploration on this topic as part of its Big Data & AI Working Group. The long-term goal and true potential of AI is to replicate the complexity of human thinking at the macro level, and then surpass it to solve complex problems - problems both well-documented and currently unimaginable in nature. Solutions necessitate enterprise-wide systems that can take in the entire picture, balance competing priorities (e.g. economics and clinical outcomes), and make contextual, complicated decisions. By any measure, we’re not there yet; in the short-term, AI is being built to surpass human capabilities via applications that augment, rather than replace, human thinking. At this stage of adoption, the “AI revolution” is best studied as a collective of individual applications, rather than the sum of different parts. Imaging applications have long been a focus, with companies like Arterys receiving FDA approval, however we further identify a growing list of top application areas for innovators. Our goals were to cut through the hype by focusing on emerging trends, drivers, and challenges in both the industry and the marketplace. We corroborated this research against a segment-by-segment analysis of adoption in pharmaceuticals/life sciences, care delivery, and the payor space.

Industry Trends: Beneath the Hype, Real Potential

“Ironically, AI suffers...[from] the “odd paradox”—AI brings a new technology into the common fold, people become accustomed to this technology, it stops being considered AI, and newer technology emerges. The same pattern will continue in the future. AI does not “deliver” a life-changing product as a bolt from the blue. Rather, AI technologies continue to get better in a continual, incremental way.” Stanford’s 100 Year Study on Artificial Intelligence, 2016

In health care, data access and computing power are the main enabling drivers of AI’s growth. The exponential uptick in data – medical, social, biochemical, device-generated and many others – provides the raw materials for the algorithms that in turn power an array of diverse applications. Another key driver is the emergence of an innovator’s mindset in healthcare and life sciences. This shift is being driven by the expansion of value-based contracts, advances in scientific techniques and consumer technology, and billions of dollars in private sector investments year over year. Of course, challenges persist between promise and reality when it comes to making technology work in the real world. In a recent survey of healthcare organizations by Royal Philips, the biggest gap between perceived progress and reality was health systems’ integration of connected technology and data sharing. Data are either unavailable or inaccessible, in part because of a lack of interoperable systems, and in part because new data-gathering tools and techniques are still emerging. The proliferation of sensors, chatbots, natural language processing (NLP), augmented reality (AR), and other technologies are creating new, important data to for the healthcare enterprise to analyze. The new breed of learning algorithms powering this work is by definition, perpetually incomplete. This has complicated the market-readiness of products, which has already proven a challenge to the budgeting, piloting, and scaling of potential solutions.

Market Trends: A Bustling Ecosystem

Other industries are more advanced in their use of AI - whether it’s Netflix, Google, or Amazon - in large part because of the availability of masses of consumer data. Finally, in both biomedical research and care delivery and planning, large amounts of data are now being generated and increasingly becoming more accessible. Partnerships are a critical enabler for industry innovators to access the training data needed to turn algorithms into solutions. Technology titans with cutting edge methods and tools typically lack the clinical expertise to build decision-making frameworks, or optimize workflows and end-user interfaces. Companies like Apple and Google have thus been busy hiring top clinical and scientific experts out of research universities. Where such clinical expertise already resides, e.g. academic medical centers, organizations are focused on enhancing their tech capabilities and machine learning talent. A large player today in terms of mindshare and ecosystem footprint is IBM’s Watson Health, who has lined up several dozen partners across oncology, pharma, payers, medical device, and health systems. While most of these partnerships are data-focused, Watson has recently commercialized their oncology products, signaling the beginnings of what looks to be a long maturation process. Competing tech giants, such as Microsoft and GE, have set out to build their own ecosystems, establishing multi-year contracts with UPMC and Partners Healthcare, respectively. Google has been active in AI across a number of its subsidiaries, including DeepMind, which inked a 5-year deal with the National Health Service (NHS) in the UK. While these partnerships may be a prerequisite for market success, they do not guarantee it; big companies and startups alike have faced implementation challenges and in other cases, are learning about the data sharing and privacy requirements that the healthcare industry is based on. Meanwhile, Apple and Amazon have been incrementally rolling out advanced computing tools to market-leading consumer footprints across web, mobile, and the Internet of Things; Both firms are positioning themselves to enter the healthcare markets in an official capacity in the years to come, Apple through their Healthkit ecosystem, and Amazon through a recently discovered healthcare innovation arm called 1492. Beyond offerings from big tech brands, hundreds of companies are offering algorithm-enhanced analysis within each segment and sub-segment of health care industry. From research ecosystems to clinical trials platforms to specific disease management niches, specialized partnerships and budding ecosystems have sprung up at every juncture.

Life Sciences: Redefining the Biological Understanding of Disease

The life sciences and pharmaceutical industry has begun leaning on AI to understand, interpret, and ultimately leverage new types of data. From an array of ‘-omics’ data to an emerging set of objective sensor-generated data, new computing techniques are helping scientists discover new drugs and develop or repurpose them with new efficiency. Companies like Wuxi NextCode and iCarbonX are focused on breaking down biochemical processes and physiology to better map the natural history of health, disease, and the diagnostic process. This work is paving the road for understanding and acting on all of the meaningful variables that impact health and disease progression over time. As this mapping becomes more sophisticated, the hope is to prevent or halt disease, or develop better, more targeted treatments. Another wave of companies have their sights set on mashing up the array of known biological drug targets and libraries of molecular compounds. By using structural algorithms and a bit of creativity, firms like BenevolentAI are mapping out the relationships between known drugs and novel indications, effectively repurposing old therapeutics for new applications to treat and cure disease Some of this work is already being applied in Lou Gehrig's disease, otherwise known as Amyotrophic Lateral Sclerosis (ALS). And of course, the industry is keen on discovering entirely new treatments. Deep Genomics aims to develop new drugs by using deep learning to explore patterns in genomic and medical data. This opens up the possibility to devise new treatment regimens and personalized medicines for cancers, autoimmune, and rare diseases. A host of solutions are aiming to further improve biomarker discovery and the clinical trial process, with patient recruitment being a particular challenge. One example is Flatiron Health, who is enabling clinical trial recruitment through their platform, while simultaneously enhancing the quality of care for cancer patients through supporting a new, data-driven model of care.

Care Delivery: Towards 360-degree View of Patient Needs

The clinical applications of AI span the gamut of inpatient care to population level efforts, with the central goal of improving health outcomes. In acute care, AI solutions are being developed to support clinical decision making in areas like specialty surgery and oncology (e.g., imaging). Several companies, e.g. AliveCor and Sentrian, are also incorporating algorithm-based feedback loops into remote monitoring platforms, to care for high-risk patients beyond facility walls. In population health, AI are fine tuning risk analysis through machine learning and use of NLP on unstructured data, such as clinical notes. Companies are leveraging these tools to identify better courses of treatment (GNS Healthcare) as well as improve adherence and engagement (Cyft). AI may very well be ushering in a world where “population health” becomes “personalized population health.” As care delivery systems take on more financial risk via value-based contracts, they will also begin adopting more back-end analytics tools to help them understand and interact with their populations. Today these use cases tend to focus on high-risk specialty areas such as cardiology and oncology. At the front end of clinical practice, ambulatory practices and health systems are introducing chatbots and other pre- and between-visit automation tools to improve the patient experience and bolster clinical care. Taken together, these clinical use cases afford a look at the impact AI is having on the automation of jobs: rather than replacing doctors or nurses, data are fueling an ever-evolving array of clinical decision support tools, at the population/panel level down to the point of care.

Payers and Consumers: Responsive Population Care and Member Experience

As payers’ role evolves along with the growth of value-based care delivery, they’ve begun leveraging AI to optimize back end workflow related to claims processing, risk analysis, and utilization management. The central goal for these applications is to achieve lower costs of care, while aligning with the marketplace trends such as member satisfaction and clinical quality. For example, Aetna recently contracted with MAP Health Management to pilot an IBM-Watson-based AI tool that predicts high-risk substance abuse patients and optimizes treatment based on both behavioral health network availability and patient preferences. Similar to providers, insurers are also applying AI to augment their traditional functions in disease management, particularly with their employer clients. The consumer experience, long a sore point across every segment of healthcare, is poised for an overhaul. Chatbots provide scalable, real-time, contextual data capture and response, enabling automation in a number of use cases both clinical and practical in nature. Advances in natural language processing (NLP), sensors, voice recognition, augmented reality (AR), sentiment analysis, and more are raising the sophistication of digital interaction and reshaping consumer use cases.

Conclusion: Cutting Through the Hype

“How do I apply [AI] technology to solve my business problem quickly, economically, and permanently in a manner that is responsive to future change?” Gartner, The Road to Enterprise AI

Today’s executives are tasked with figuring out how to unlock and unleash AI’s potential in the short-term, while simultaneously planning for a longer-term impact. Our research revealed a technological movement in its early days, but with strong potential to upend product design, translational research, health care delivery, and legacy business models. Further, the vary nature of applying new methods to various use cases, has distinctively made AI-related transformation efforts into a “team sport.” Many perspectives, skills, and capabilities are often required to maximize learning and value, and ultimately the ROI of emerging solutions. Below are a handful of takeaways for progressive leaders at forward-thinking organizations planning on incorporating AI into their business transformation efforts.

Define Your Problem

Despite the breadth of coverage – in part because of it – there is not one consensus definition of AI. For all the fervor over generalized AI, the technology is best defined through specific use cases, rather than broad concepts such as automation or data analysis. A tightly defined problem list can serve as an important launchpad for the partner selection process. Such a defined problem should also highlight data requiremens, alongside the expected quality and completeness of the data, to ensure appropriate levels of analysis.

Develop Internal Capabilities

With so many companies touting different AI methods, showing variable published results, and in some cases being from fields outside of research and healthcare, it is important to be able to evaluate these various solutions with a critical eye. Beyond simply buying a solution or funding a pilot however, organizations should be planning heavily for their internal investments in a data science or AI team to help with everything from vendor selection, auditing algorithms and understanding training data, to pilot implementation and project scaling. As with any innovation effort, convening a diverse set of stakeholders increases the chances of a positive outcome.

Temper Expectations

AI is more evolution than revolution in the near term. In many sub-segments, such as diabetes or behavioral health, new offerings like AI-powered care plans, virtual nurses, and self-management apps are still competing with live health coaches. Other applications remain in the thick of the “training phase,” despite their vendors’ polished storytelling. Moreover, at this early stage, there are likely many use cases your teams come up internally that lack commercial solutions, although their methods and tools may be relevant. In these cases, do your homework, and enable internal innovation groups, skunkworks prototyping, and the like. When nurtured and allowed to grow, an experimentation mindset will be rewarded with learnings at a minimum, and ideally, new understanding.

Partner Creatively and Proactively

Beyond thinking about partnerships as a source of training data and end-users, vendors and customers should seek out opportunities to “skate to where the puck is headed.” Payers like Aetna have shown an early willingness to pilot reimbursement schemes built around AI-enhanced tools. Enabling better patient experiences are relevant in any field, from clinical trials to member retention. The world of the patient, consumers, and individuals stretches far beyond the traditional definitions of healthcare and biomedical data; thinking outside of the box and merging life data, social determinants of health, environmental context, can all help raise additional areas of inquiry or redefine research methodologies.